Classification relates to the process of categorizing new or unknown information based on already known categorizations of similar information.
For example, a classifier may perform the classification. The classifier may define various classes that relate to different types of information. An e-mail classifier, for instance, may separately define a “legitimate e-mail” class and a “spam e-mail” class. To improve classification accuracy, a data set containing samples that are known to belong to certain classes may be used to “train” the classifier. Thus, in the e-mail classifier example, a data set containing both known legitimate e-mails and spam e-mails may be utilized to train the classifier over many iterations so that it can know how to accurately categorize a new, unknown e-mail as legitimate or spam.
As a classifier gets more complex, however, the classes may exhibit varying levels of performance or accuracy during training. Accordingly, there is a need to efficiently identify and rehabilitate inaccurate or underperforming classes during the training process to improve the overall effectiveness and accuracy of the classifier.